2014 4th International Conference on Artificial Intelligence with Applications in Engineering and Technology 3GPP Technologies: Load Balancing Algorithm and InterNetworking Belal Abuhaija Faculty of Computers and Information Technology The University of Tabuk Tabuk, Saudi Arabia babuhaija@ut.edu.sa Abstract Multiple Radio Access technology (RATs) is the way forward by the cellular operators. Such coexistence between the different technologies can introduce many operational coordination and management problems. Long Term Evolution (LTE) advanced and otherwise has been deployed in the same cell site as of Universal mobile Telecommunication (UMTS) cells. LTE and UMTS networks have different Radio Resources Management (RRM) functionality and network architecture. Therefore; most operators have considered common radio resources management functionality in more than one way in order to use radio resources efficiently. Such deployment has to be concerned with many technical issues; among others, the internet traffic and load balancing among multi RAT. After all; the network will be connected to the World Wide Web in one form or another. In this contribution, we are devising a new algorithm that considers cell selection based on internet traffic modeling approaches. At the same time we address the issue of load balancing among the different RATs. Keywords- LTE; UMTS;Deployment; Traffic classes; Algorithms. I. INTRODUCTION Mobile operators cannot escape the future deployment and coexistence of LTE in the same cell site as UMTS. Several technical issues; such as; the interconnection and interaction between the Word Wide Web (WWW) and the cellular operators networks as well as load balancing of the operator internal network are issues that needs to be addressed efficiently. Multi-access network planning and radio resource management, which are essential aspects in the multi-radio/multi-access technology deployment, are further complicated by the requirements to support diverse traffic classes and services that are standardized by 3GPP community [1]. Many deployment scenarios have been considered by 3GPP. One of such scenario is illustrated in figure 1. The RATs are deployed with single coverage to enhance the quality of the services offered to the customers. Several technical issues emanate from such deployment. Two technical issues are of concern; the load balancing among the different rates and the integration with the internet traffic. The integration with the internet traffic is a complex issue; because in such deployment scenario the network has three reference points to the outside internet service providers. Such issue is diversified by the traffic services that are offered by the cellular operators in conformance with the 3GPP standards [1]. Figure 1: Deployment of Multi-RATs Traffic classes in the form of interactive services such as web browsing or in the form of file transfer services (FTP) or in the form of streaming services like video or audio download or the more stringent services such as legacy voice, VOIP or video conferencing. Such services except for legacy voice, requires that the cellular network interact with the Internet service providers. Therefore, modeling the above services in the internet should serve as a guide for the cellular operators to model their networks to handle such data flow. As far as Load balancing is concern; this issue should be addressed in idle mode as well as connected mode. Internetworking among UMTS and LTE networks has been the subject of several research scenarios and proposals. Such proposals have been mainly focused on two scenarios: small cell scenarios and macro cell scenarios. In this research we are limiting our approach to macro cell deployment. We are also devising an algorithm for load balancing in connected mode but based on the fact that data traffic is identical to that of the internet traffic. Therefore, we are modeling the data traffic (FTP and HTTP) as specified in [2-4]. In the following section, Section II, we present a literature review, in Section III a brief description of the technologies and the internetworking scenario, in section IV, a description of the proposed load balancing algorithm is presented, in section V we describe the simulation environment and in Section VI we present the simulation results and discussion, and in Section VII we conclude the paper and provide hints for future work. 978-1-4799-7910-3/14 $31.00 2014 IEEE DOI 10.1109/ICAIET.2014.51 267
II. LITERATURE REVIEW Smart phones and applications can influence the performance of cellular networks in more than one way. Traffic characteristics understanding is an essential issue when designing the infrastructure of mobile networks. Data traffic applications and consumption has been doubling every year with no sign of slowing down. On the contrary the more offering of unlimited data plans by the operators the more data consumption there is by the customers. This trend is almost worldwide. The majority of broadband traffic is derived by the availability of smart phones and as well as the variety of applications. The more complex the devices and applications become the more data traffic is consumed by the customers. Video traffic has been identified as the main source of data traffic on the cellular networks including mobile TV and video telephony. Mobile networks primarily designed to provide voice services. However, as the demand for more data traffic and capacity has been continuously growing and the availability of smart phones has driven operators to deploy micro and Pico cells. Another solution for increasing capacity is to evolve mobile broadband networks into heterogeneous networks that leverage an evolved macro cell layer complemented with integrated small cells. Such deployment is illustrated in figure 2. Therefore, inter RAT coordination aims at improving the radio resources coordination efficiency in the form of load balancing. Load balancing can improve network capacity by steering the traffic to the appropriate RAT as well as guaranteeing the QoE across RATs. Inter RAT mobility from LTE to 3G is employed when there is a limited coverage by LTE or when the system needs to balance the load in the networks. However, the mobility from 3G to LTE is triggered by traffic management policies in the system. We are confining our approach to study full overlap of UMTS and LTE networks. UMTS Macro cell Scenario 3a LTE Macro Figure 2: deployment scenario [5] Data traffic of the cellular networks is no different from the internet data traffic; after all the traffic characteristics are the same. Packet switched networks such as the internet relies mostly on two protocols; User Data Gram (UDP) and Transmission Control Protocol (TCP). The first does not require establishing a session to in order to start transmission, while the second requires establishing a connection to the remote site in order to exchange data. Therefore, web browsing which utilizes Hyper Text Transfer Protocol (HTTP) does not require establishing a connection and uses UDP protocol for data transfer. However, file transfer protocol (FTP) utilizes TCP and require a session connection before exchanging data. Modeling FTP traffic and HTTP traffic follows long tail or heavy tail distribution; such as lognormal distribution or Pareto distribution. Such distributions are characterized by the fact that the data samples generated are mainly of small size; however, what influence the distribution a great deal is the fact that the small number of large data generated by the distribution has much more influence on the mean and variance of the distribution. Therefore and according to [2], the FTP traffic which is used for download or streaming services is modeled as illustrated in table 1. The FTP session is modeled as truncated log normal with µ and σ as illustrated in table 1. While the reading time is modeled as negative exponential distribution with λ = 1/180s. Parameter File Size S Table 1: FTP Distribution Parameters Distribution Truncated lognormal 2 (ln( x) µ ) ƒ( x) = (1 σx 2π )exp( ) 2 2σ μ = 14.45 and σ =.35 and x >0. Mean = 2Mbytes, standard deviation =.722Mbytes and Maximum = 5Mbytes Reading Exponential distribution with λ = 180 s. time TD The HTTP traffic is categorized as interactive services by 3GPP standards [1]. The session length is following the same truncated log normal distribution as in table 1, with the exception that it is truncated between 100Bytes and 2Mbytes. HTTP distribution parameters are µ= 8.35 and σ = 1.37. The parsing time and reading time is negative exponentially distributed with λ =.033 and 7.69 respectively. The system shall also cater to the most basic services of the cellular system. The voice service is modeled using the negative exponential distribution with µ= 1/210s and consuming 25kbps from the bandwidth. While the video streaming service is modeled using the negative exponential distribution as will but with parameters of µ = 1/3600s and 384kbps as guaranteed from the bandwidth. Inter arrival time of the sessions are negative exponential with different values of λ. From the above; the merge between the inter RAT coordination and the services requested by the customers could not be more complicated. Load balancing between RATs should be based on the length of the session and the appropriate RAT for the service. 268
III. 3GPP TECHNOLOGIES In this paper we are considering two main technologies; LTE and UMTS. It is the belief of the author that this can be scalable to all technologies specified by 3GPP community. In the following we briefly describe the technologies involved as well as the internetworking overview of the technologies. A. UMTS UMTS technology is based on Wide Code Division Multiple Access (WCDMA) radio transmission standard and it employs 5Mhz channel bandwidth. This bandwidth has the capacity to carry 100 voice channels simultaneously through the use of orthogonal codes. Such codes are unique to each user to be able to separate the channels. The basic sequence of the codes is the chip and denoted as the spreading factor. The codes are of variable length varies 4-512 in the downlink in the code tree; while the codes length varies from 4-256 in the uplink. The length of the code depends on the bit rate required. The higher the bit rate the lower the spreading factor. In other words, the spreading codes are related to the required service. When using the orthogonal codes it is worth mentioning that if a code is used down the tree of codes; it is blocked from usage by any other user. Some of these codes are reserved for signaling by the radio network controller (RNC) or by the core network. The core network has two domains; the circuit switched domain and the packet switch domain. The UTRAN is connected to the SGSN through the Iu-ps interface; while it is connected to the Mobile Switching Center (MCS) through the Iu-cs interface. Legacy voice calls are directed to the Iu-cs domain; while the data services are directed to the Iu-ps interface [7]. The UMTS resources are the number of codes utilized by the UTRAN and the corresponding data rate that it carries [7]. B. LTE LTE network and technology is fundementally different than of UMTS. The recources depends on the bandwidth utilized which can range from 1.4Mhz to 20Mhz. It emplyes resource blocks; each resource block is 180Khz, divided ito 12 subcarriers and each subcarrier is 15Khz. Each subcarrier can carry either 6 or 7 signalling elements depeding on the cyclic prefix used. The system employes orthogonal frequency division multiple access (OFDMA) in the frequency domain in the downlink and the length of the frame is.5ms in the time domain. In other words the scheduling technique is based on frequency domain and time domain. However, the scheduling is done on 1ms intervals in the time domain. The standards are specifying that the technology can carry up to 300Mbps in the downlink and up to 100 Mbps in the uplink in the 20Mhz bandwidth. The latency should be less than 5ms for the user plain with high spectral effeciency [8-9]. LTE only support one domain which is packet switched dmain. If a circuit switched applications and services are required then it should be implemented through IP domain. The resources in LTE are quantified by the number of resource blocks (180Khz) available in the cell. C. Internetworking The core networks in LTE and UMTS are fundamentally different. Both technologies have separate connection to the public data network (PDN) or the functionality of the GGSN and PDN-GW as in [6] and illustrated in Fig.3. With a separate connection between the GGSN and the internet the interface is called Gn which is not shown in Fig 3. LTE networks are flat all IP packet switched network while UMTS networks has two domains, as such it requires two connections one to the packet switched network domain in Iu-PS and Iu-CS for the circuit switched network domain. This is crucial when internetworking is studied in cellular networks. For pure legacy voice services the UE should be connected directly to the circuit switched domain and as a matter of fact directing such calls to UMTS would yield better user experience as the UE will not go through the heavy signaling required to establish a VOIP session. Two solutions have been proposed to provide voice services over LTE networks. One is called circuit switched fallback when the LTE network is not yet matured in deployment and the other is to utilize IP Multimedia Subsystem (IMS) networks. All VOIP sessions which is called VOLTE; the signaling has to go through the IP Multimedia Subsystem (IMS) network. For all data services such as streaming and file download or even browsing the internet; the decision should depend on the size of the download. In order to avoid heavy signaling back and forth between Radio Access Technologies a mechanism should be placed to attach the service to the appropriate RAT. Figure 3: The Interworking Scenario IV. THE PROPOSED LOAD BALANCING ALGORITHM Load balancing in multi-rat scenario depends on many parameters; first, the load of the RAT at the time of the service request. The load can be quantified as a percentage of the resources of each RAT. As explained in in section III- 269
A and III-B, the resources in LTE and UMTS are quantified in different way. The reason has to do with the core technologies that both RATs are employing. Therefore, it would be more realistic to consider load balancing based on the percentage of resources consumed by each RAT. Second, the traffic profile that the users are requesting. The traffic profile depends on the application that the user is accessing and on the service. For example, when the user is requesting a voice service, it would be in-efficient to direct the call to LTE RAT even though that the UE might be capable of supporting VOLTE. However, if the requested service is a data service and the user profile indicates that the user is downloading heavy traffic, it would be inefficient to direct the call to UMTS RAT. Third, when measuring quality of service (QoS), most of the researchers are relying on the signal to interference plus noise ratio (SINR) only. The measure of signal strength in UMTS depends on the CPICH which is a measure of Ec/I0, while the SINR in LTE depends on Reference Signal Received Power (RSRP) or Even Received Signal Strength Indicator (RSSI) in LTE. Both of these measures by themselves are not adequate to ensure the Quality of Service. Therefore, all three facors listed above should be considered when load balancing algorithm is designed. Load balancing shall be considered in two ways, one for UE in idle mode and another for UE in connected mode. We are focusing the algorithm on UE in connected mode. Therefore and based on the above description the algorithm is depicted in figure 4. The new arrival to the system does the radio measurements for both LTE and UMTS. Let us keep in mind that we are realizing the UE that can access both technologies. Once the radio aspect is determined then the resources consumption of both RATs are determined. Let us keep in mind that such information is available to the RATs through the Mobility Management Entity (MME) on the LTE side and the Radio Access Controller (RNC) on the UMTS side and assuming that the information is up to date. Once that is determined then we turn to the customer service profile. If the service is legacy voice then the sole provider of that service is UMTS through the circuit switched domain (not shown in figure 3). If the service request is data, then the customer profile is checked, is the customer heavy download user or light download user. In any case the service is directed to LTE regardless of the load measurements if the user is heavy download. Otherwise, for data users we check the load of the RATs. If the percentage of LTE load is more than the percentage load of UMTS then the service is turned to LTE. This might look like it is counter intuitive, but LTE has higher spectral efficiency and Figure 4: The Load Balancing Algorithm capacity than UMTS. Therefore, it would make sense to make it the first choice. Finally, the light load customers are directed to UMTS RAT if capacity if available. Otherwise the call is blocked from the system. 270
V. THE SIMULATION ENVIRONMENT A customized version of the Discrete Event simulator described in [10] and depicted in figure 5. The simulator was designed to implement 5 different environments, free space, rural, suburban, urban and dense urban. The deployment of the cells can be studied in any environment and under any deployment. The simulator was designed as class based in order to be flexible to any new and envisioned scenarios. The radio resources measurements can be realized for all 3GPP technologies namely, LTE, UMTS and GSM. Despite the fact that the algorithm is limited to two RATs but it can be scalable to any number of RATs and to any number of cells. The radio propagation and measurements are in line with the standards and the wellknown technology books [7-11]. The customer profile is determined once the user is created in the simulator. If the service is voice service then the user considered as UMTS user. However, if the user is a data user then the service profile is determined. In other words, is the service requested HTTP or FTP and if the user is heavy load user or light load user. The simulator then check the load of LTE and UMTS based on the percentages consumed from the resources. Up on collecting all this information, the simulator decides on the best RAT to service the customer. Figure 5: The simulator environment VI. CONCLUSION In this contribution, a load balancing algorithm has been designed and presented. Three criteria have been realized, the radio measurements; the network load of each RAT and the service of the application requested by the user. A description of the simulator to carry out the algorithm and collect the results has been introduced. The modeling of the services to be in line with the modeling of the internet services has been outlined. Future work is to carry the studies based on the algorithm and determine the best case scenario for the Multi-RAT deployments. ACKNOWLEDGMENT The author would like to acknowledge the financial support for this work from The Deanship of Scientific Research (DSR), University of Tabuk, Saudi Arabia under grant Number S-100-1435. REFERENCES [1] 3rd Generation Partnership Project (3GPP), Technical Specification Group Services and System Aspects; Service Aspects, Service and Service Capabilities, 3GPP TS 22.105 V11.0.1 (2012-11), Release 11. [2] 3GPP2-C50-EVAL 2001 022-0, HTTP and FTP Traffic Models for 1xEV-DV Simulations, 2001. [3] http://ieee802.org/16/tgm/docs/80216m-08_004r2.pdf. [4] F. Barcelo and J. Jordan, Channel holding time distribution in cellular telephony, in: The 9th International Conference on Wireless Communications (Wireless'97), Vol. 1, Alberta, Canada (9 11 July 1997) pp. 125-134. [5] 3GPP TR 37.852, 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; RAN enhancements for UMTS/HSPA and LTE interworking (Release 12). [6] Long Term Evolution, A technical overview; http://www.motorola.com/mcne/techdocs/lte_technical.pdf. [7] H. Holma and A. Toskala, "WCDMA for UMTS-HSPA Evolution and LTE". John Wiley & Sons Ltd, 2007. [8] H. Holma and A. Toskala, LTE for UMTS-OFDMA and SC-FDMA based radio access. Wiley, 2009. [9] S. Sesia, I. Toufik, and M. Baker, Lte the umts long term evolution, From Theory to Practice, published in, vol. 66, 2009. [10] B. Abuhaija and K. Al-Begain, Enhanced common radio resources managements algorithm in heterogeneous cellular networks, in Third International Conference on Next Generation Mobile Applications, Services and Technologies, 2009. NGMAST 09, pp. 335 342. [11] 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access ( UTRA); Base Station (BS) radio transmission and reception, 3GPP TS 36.104 (Release 12). 271